One rare type of cancer that slowly grows and affects the human blood cells is termed as Waldenstrom Macroglobulinemia (WM). With the formation of excess WBCs in the bone marrow region, WM occurs. Healthcare industries can provide better treatments to eliminate the symptoms that cannot be cured. Everyone in the healthcare industry knows that genetic mutations trigger WM but do not know what causes the mutations. The risk factors that lead to the multiplication of WBCs in the bone marrow regions causing WM have been identified. Healthcare industries are trying to provide better treatment to save patients. When detected at an earlier stage, the possibilities of curing the disease are bright. Several earlier research works have proposed conventional algorithms and software models for analyzing healthcare data related to WM. But the accuracy is poor and not efficient in terms of cost and time. This paper proposed an Artificial Immune System (AIS) algorithm for analyzing the genomic dataset and identifying Waldenstrom's Macroglobulinemia or its symptoms. The experiment is carried out in Python software for verifying the results. By comparing the experimental results with the other methods, the performance is evaluated. The comparison shows that the proposed AIS algorithm outperforms the others.